from flask_restful import Resource, reqparse from flask import jsonify,request from bridge.context import ContextType import requests,json from wechat import gewe_chat from voice.ali.ali_voice import AliVoice from common import utils,redis_helper,memory,kafka_helper from common.log import logger import openai import xml.etree.ElementTree as ET import os from voice import audio_convert class MessagesBKResource(Resource): def __init__(self): self.parser = reqparse.RequestParser() def post(self): msg = request.get_json() logger.debug(f"Received message: {msg}") if 'Data' in msg: msg_data=msg.get("Data") msg_type=msg_data.get("MsgType") if msg_type == 1:#ContextType.TEXT: # 文字 msg_content=msg_data["Content"]["string"] #print(msg_content) logger.info(msg_content) app_id=msg["Appid"] wxid=msg["Wxid"] from_wxid=msg_data["FromUserName"]["string"] to_wxid=msg_data["ToUserName"]["string"] token_id="f828cb3c-1039-489f-b9ae-7494d1778a15" hash_key = f"__AI_OPS_WX__:MESSAGES:{wxid}" if wxid == from_wxid: #主动发送消息 logger.info("Active message sending detected") gewe_chat.wxchat.save_contacts_brief_to_cache(token_id,app_id,wxid,[to_wxid]) callback_to_user=msg_data["ToUserName"]["string"] input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}] input_message=utils.dialogue_message(from_wxid,to_wxid,input_wx_content_dialogue_message) kafka_helper.kafka_client.produce_message(input_message) logger.info("发送对话 %s",input_message) else: callback_to_user=msg_data["FromUserName"]["string"] prompt={"role": "user", "content": [{ "type": "text", "text": msg_content }]} messages_to_send=get_messages_from_cache(hash_key, prompt) # 收到的对话 input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}] input_message=utils.dialogue_message(callback_to_user,wxid,input_wx_content_dialogue_message) kafka_helper.kafka_client.produce_message(input_message) logger.info("发送对话 %s",input_message) cache_data = memory.USER_INTERACTIVE_CACHE.get(wxid) if cache_data and cache_data.get('interactive'): messages_to_send=[{"role": "user", "content": msg_content}] res=fast_gpt_api(messages_to_send,wxid) reply_content=res["choices"][0]["message"]["content"] description = '' userSelectOptions = [] if isinstance(reply_content, list) and any(item.get("type") == "interactive" for item in reply_content): for item in reply_content: if item["type"] == "interactive" and item["interactive"]["type"] == "userSelect": params = item["interactive"]["params"] description = params.get("description") userSelectOptions = params.get("userSelectOptions", []) values_string = "\n".join(option["value"] for option in userSelectOptions) if description is not None: memory.USER_INTERACTIVE_CACHE[wxid] = { "interactive":True } reply_content=description + '------------------------------\n'+values_string elif isinstance(reply_content, list) and any(item.get("type") == "text" for item in reply_content): memory.USER_INTERACTIVE_CACHE[wxid] = { "interactive":False } text='' for item in reply_content: if item["type"] == "text": text=item["text"]["content"] if text=='': # 去除上次上一轮对话再次请求 cache_messages_str=redis_helper.redis_helper.get_hash_field(hash_key,"data") cache_messages = json.loads(cache_messages_str) if cache_messages_str else [] if len(cache_messages) >= 3: cache_messages = cache_messages[:-3] redis_helper.redis_helper.update_hash_field(hash_key,"data",json.dumps(cache_messages,ensure_ascii=False)) messages_to_send=get_messages_from_cache(hash_key, prompt) res=fast_gpt_api(messages_to_send,wxid) reply_content=res["choices"][0]["message"]["content"] else: reply_content=text else: memory.USER_INTERACTIVE_CACHE[wxid] = { "interactive":False } reply_content=res["choices"][0]["message"]["content"] print(f'token_id {token_id}') print(f'app_id {app_id}') print(f'touser: {callback_to_user}') # print(f'towxuser:{towxuser}') print(reply_content) gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,reply_content) get_messages_from_cache(hash_key, {"role": "assistant", "content": reply_content}) # 回复的对话 input_wx_content_dialogue_message=[{"type": "text", "text": reply_content}] input_message=utils.dialogue_message(wxid,callback_to_user,input_wx_content_dialogue_message,True) kafka_helper.kafka_client.produce_message(input_message) logger.info("发送对话 %s",input_message) if msg_type == 3: #图片 token_id="f828cb3c-1039-489f-b9ae-7494d1778a15" app_id=msg["Appid"] callback_to_user=msg_data["FromUserName"]["string"] msg_content=msg_data["Content"]["string"] print(f'token_id {token_id}') print(f'app_id {app_id}') print(f'touser: {callback_to_user}') # print(res_content) wxid=msg["Wxid"] hash_key = f"__AI_OPS_WX__:MESSAGES:{wxid}" wx_img_url=gewe_chat.wxchat.download_image_msg(token_id,app_id,msg_content) oss_access_key_id="LTAI5tRTG6pLhTpKACJYoPR5" oss_access_key_secret="E7dMzeeMxq4VQvLg7Tq7uKf3XWpYfN" oss_endpoint="http://oss-cn-shanghai.aliyuncs.com" oss_bucket_name="cow-agent" oss_prefix="cow" img_url=utils.upload_oss(oss_access_key_id, oss_access_key_secret, oss_endpoint, oss_bucket_name, wx_img_url, oss_prefix) prompt={ "role": "user", "content": [{ "type": "image_url", "image_url": {"url": img_url} }] } get_messages_from_cache(hash_key, prompt) gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,'已经上传了图片，有什么可以为您服务') logger.debug(f"Uploaded image URL: {img_url}") wx_content_dialogue_message=[{"type": "image_url", "image_url": {"url": img_url}}] input_message=utils.dialogue_message(wxid,callback_to_user,wx_content_dialogue_message) kafka_helper.kafka_client.produce_message(input_message) logger.info("发送对话 %s",input_message) if msg_type == 34: # 语音 token_id="f828cb3c-1039-489f-b9ae-7494d1778a15" callback_to_user=msg_data["FromUserName"]["string"] app_id=msg["Appid"] msg_content=msg_data["Content"]["string"] msg_id=msg_data["MsgId"] wxid=msg["Wxid"] hash_key = f"__AI_OPS_WX__:MESSAGES:{wxid}" print(f'token_id {token_id}') print(f'app_id {app_id}') print(f'touser: {callback_to_user}') print(f'msg_id:{msg_id}') file_url=gewe_chat.wxchat.download_audio_msg(token_id,app_id,msg_id,msg_content) react_silk_path=utils.save_to_local_from_url(file_url) react_wav_path = os.path.splitext(react_silk_path)[0] + ".wav" audio_convert.any_to_wav(react_silk_path,react_wav_path) react_voice_text=AliVoice().voiceToText(react_wav_path) messages=get_messages_from_cache(hash_key, {"role": "user", "content": react_voice_text}) ai_res=fast_gpt_api(messages,wxid) ai_res_content=ai_res["choices"][0]["message"]["content"] reply_text_voice=AliVoice().textToVoice(ai_res_content) reply_text_voice_path=os.path.join(os.getcwd(), reply_text_voice) reply_silk_path = os.path.splitext(reply_text_voice_path)[0] + ".silk" reply_silk_during=audio_convert.any_to_sil(reply_text_voice_path,reply_silk_path) # print(int(reply_silk_during)) # print(reply_silk_path) oss_access_key_id="LTAI5tRTG6pLhTpKACJYoPR5" oss_access_key_secret="E7dMzeeMxq4VQvLg7Tq7uKf3XWpYfN" oss_endpoint="http://oss-cn-shanghai.aliyuncs.com" oss_bucket_name="cow-agent" oss_prefix="cow" file_path=reply_silk_path file_url = utils.upload_oss(oss_access_key_id, oss_access_key_secret, oss_endpoint, oss_bucket_name, file_path, oss_prefix) print(file_url) res=gewe_chat.wxchat.post_voice(token_id,app_id,callback_to_user,file_url,int(reply_silk_during)) # 删除临时文件 os.remove(react_silk_path) os.remove(react_wav_path) os.remove(reply_text_voice_path) os.remove(reply_silk_path) get_messages_from_cache(hash_key, {"role": "assistant", "content": ai_res}) if msg_type == 49: msg_content_xml=msg_data["Content"]["string"] root = ET.fromstring(msg_content_xml) type_value = root.find(".//appmsg/type").text if type_value==57: # 引用消息 ''' # 判断此类消息的逻辑：$.Data.MsgType=49 并且 解析$.Data.Content.string中的xml msg.appmsg.type=57 ''' wxid=msg["Wxid"] hash_key = f"__AI_OPS_WX__:MESSAGES:{wxid}" app_id=msg["Appid"] callback_to_user=msg_data["FromUserName"]["string"] # towxuser=touser.get("") # token_id="ce50e2c376c843a9a281af3a1a0f4420" token_id="f828cb3c-1039-489f-b9ae-7494d1778a15" prompt={"role": "user", "content": [{ "type": "text", "text": msg_content }]} # 收到的对话 messages_to_send=get_messages_from_cache(hash_key, prompt) input_wx_content_dialogue_message=[{"type": "text", "text": msg_content}] input_message=utils.dialogue_message(callback_to_user,wxid,input_wx_content_dialogue_message) kafka_helper.kafka_client.produce_message(input_message) logger.info("发送对话 %s",input_message) # 回复的对话 res=fast_gpt_api(messages_to_send,wxid) reply_content=res["choices"][0]["message"]["content"] input_wx_content_dialogue_message=[{"type": "text", "text": reply_content}] input_message=utils.dialogue_message(wxid,callback_to_user,input_wx_content_dialogue_message,True) kafka_helper.kafka_client.produce_message(input_message) logger.info("发送对话 %s",input_message) get_messages_from_cache(hash_key, {"role": "assistant", "content": reply_content}) gewe_chat.wxchat.post_text(token_id,app_id,callback_to_user,reply_content) return jsonify({"message": "微信回调成功"}) def get_messages_from_cache(hash_key,object:object)->object: messages=redis_helper.redis_helper.get_hash(hash_key) wxid=hash_key.split(':')[-1] if not messages: messages=[{"role": "system", "content": ""}] messages.append(object) redis_helper.redis_helper.set_hash(hash_key,{"data":json.dumps(messages,ensure_ascii=False)},3600) else: messages_str=redis_helper.redis_helper.get_hash_field(hash_key,"data") messages = json.loads(messages_str) if messages_str else [] #判断是否含有图片 last_message = messages[-1] # print(last_message) # print('~~~~~~~~~~') # 获取 content 并判断其是否为列表 content = last_message.get("content", []) if isinstance(content, list) and content: last_content_type = content[-1].get("type") if last_content_type == 'image_url': content.append(object['content'][0]) messages[-1]['content']=content else: messages.append(object) else: messages.append(object) # messages.append({"role": "user", "content": msg_content}) #messages.append(object) # print(messages) redis_helper.redis_helper.set_hash(hash_key,{"data":json.dumps(messages,ensure_ascii=False)},3600) return messages def fast_gpt_api(messages:list,session_id:str): #api_key="sk-tdi7u0zuLsR0JpPMGBeFZxymOpL0zoFVafX8EEEvEakIDAGQ22NyQ6w" api_key="sk-uJDBdKmJVb2cmfldGOvlIY6Qx0AzqWMPD3lS1IzgQYzHNOXv9SKNI" api_url = "http://106.15.182.218:3000/api/v1/chat/completions" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {api_key}" } data={ "model": "", "messages":messages, "chatId": session_id, "detail": True } print(json.dumps(data,ensure_ascii=False)) logger.info("[CHATGPT] 请求={}".format(json.dumps(data, ensure_ascii=False))) response = requests.post(url=api_url, headers=headers, data=json.dumps(data), timeout=600) response.raise_for_status() response_data = response.json() logger.info("[CHATGPT] 响应={}".format(json.dumps(response_data, separators=(',', ':'),ensure_ascii=False))) print(response_data) return response_data